Grundlegendes Praxisprojekt - WS 24/25
Dr. Fabian Scheipl, Dr. Sabine Hoffmann, Daniel Schlichting
2024-02-13
1. Overview & Terminology
2. Data Analysis
3. Summary
1. Overview & Terminology
2. Data Analysis
3. Summary
VR-Training: Adapting Virtual Reality Training Applications by Dynamically Adjusting Visual Aspects
Scenario:
- Users move and place parcels in a VR warehouse using controllers.
- Visual cues: dynamic lighting and color guidance.
Motivation:
- Static training doesn’t fit everyone: Too easy → boredom, Too hard → anxiety
- Adaptive VR training balances difficulty to improve outcomes.
Adaptive Features:
- Tracks user behavior (head movement) and performance (time, errors).
- Adjusts lighting, object colors, etc., after each training round.
1. Overview & Terminology
2. Data Analysis
3. Summary
1. Are there differences between the 2 data cohorts, Linne and Dame?
→ Despite demographic and protocol differences, the two cohorts are similar enough to be combined for meaningful analysis
2. How are the stress indicators and the physiological measurements related?
3. Does this correlation change over the rounds?
4. Are there subgroups within the test subjects that stand out from the rest?
1. Overview & Terminology
2. Data Analysis
- Cohorts comparison: Similarities and Differences
- Relationship between Stress indicators and Physiological Measurements
- Relationship over the rounds
- Subgroups and Outliers
3. Summary
1. Are there differences between the 2 data cohorts, Linne and Dame?
2. How are the stress indicators and the physiological measurements related?
⇒ Values of the correlation vary from -0.15 to 0.1, indicating very weak relationship between the stress indicators and the physiological measurements
3. Does this correlation change over the rounds?
4. Are there subgroups within the test subjects that stand out from the rest?
1. Overview & Terminology
2. Data Analysis
- Cohorts comparison: Similarities and Differences
- Relationship between Stress indicators and Physiological Measurements
- Relationship over the rounds
- Subgroups and Outliers
3. Summary
1. Are there differences between the 2 data cohorts, Linne and Dame?
2. How are the stress indicators and the physiological measurements related?
3. Does this correlation change over the rounds?
→ The correlation between physiological measurements and stress indicators shows minimal changes across the rounds, correlation values mostly falling within the range of -0.2 to 0.2 and no consistent trend observed.
4. Are there subgroups within the test subjects that stand out from the rest?
1. Overview & Terminology
2. Data Analysis
- Cohorts comparison: Similarities and Differences
- Relationship between Stress indicators and Physiological Measurements
- Relationship over the rounds
- Subgroups and Outliers
3. Summary
The plot shows that as cognitive load increases, RMSSD decreases, with high HRV defined as RMSSD values above the 0.9 quantile, indicating lower autonomic flexibility under stress.
1. Are there differences between the 2 data cohorts, Linne and Dame?
2. How are the stress indicators and the physiological measurements related?
3. Does this correlation change over the rounds?
4. Are there subgroups within the test subjects that stand out from the rest?
Dame Males (Percieved Stress > 4): High cognitive load with low heart rate.
Linne Adaptive Males: High cognitive load (Q1) with low to moderate heart rate.
Age 20-30: High RMSSD at low stress levels in the combined cohort.
Outlier: A 51-year-old subject (the only subject >50 y/o) with lower cognitive load than the majority of the cohort and with the highest RMSSD in the cohort.
Outlier: A Subject in the NonAdaptive training version in Linne cohort with percieved stress > 3 and very low heart rate.
1. Overview & Terminology
2. Data Analysis
3. Summary